Representations of solar photovoltaic triple-diode models using artificial hummingbird optimizer. Issue 4 (21st December 2022) - Shaheen, Abdullah; El-Sehiemy, Ragab; El-Fergany, Attia; Ginidi, Ahmed
ABSTRACT: The true modeling of the solar photovoltaic units can boost their performances. However, on account of the lack of accurate solar cell parameters, cell modeling is erroneous. This is because the required parameters for modeling a trustworthy solar photovoltaic cell are not given in the manufacturer's data sheets. As a consequence, it is significant to accurately estimate the essential parameters of solar photovoltaic triple-diode models. Diverse optimization techniques have addressed this problem; nevertheless, due to premature convergence and local minima, most of the techniques obtain suboptimal results. Accordingly, artificial hummingbird technique (AHBT) is attempted for estimating SPV uncertain parameters in this current effort. Furthermore, the AHBT approach improves quality of solution by preserving a history of past locations that may be compared to the present ones. To demonstrate the competency of the AHBT, its performance is compared to other well-known frameworks and newly developed techniques (e.g. African vulture's optimization technique, Tuna swarm technique, and teaching learning studying-based technique) that are applied for the first time in this article. Some actual results of the minimum values of the errors in measured and estimated currents by employed ABHT method are 1.6945 mA for STM6-40/36 module, 0.5144 mA for mSi cell, and 0.4447 mA for KC200GT module. The solar photovoltaic modules are tested with varying temperature and irradiance sunshine levels. In addition to this, the calculated parameters are compared to experimental findings, including statistical analysis to validate the presentation of the AHBT. At last stage of this current effort, the principal performance of triple-diode models is investigated under varied operating temperatures and varied sun irradiances as well. Based on the findings, it can be concluded that the AHBT are effective at estimating solar photovoltaic models. The performance of a solar photovoltaic generating unit might be improved by precise modeling.